The AI Technical Lead will be the technical backbone of the AI CoE, responsible for designing scalable AI architectures, leading AI engineering teams, and delivering enterprise-grade AI solutions. The role requires expertise in GenAI, LLMs, RAG, Machine Learning, API integration, cloud platforms, and enterprise application architecture.

The ideal candidate is a hands-on technology leader who can design complex AI solutions while mentoring engineers and collaborating closely with business, product, and enterprise architecture teams.

Key Responsibilities

AI Solution Architecture

- Design scalable, secure, and production-ready AI solution architectures.
- Define enterprise AI reference architecture and engineering standards.
- Build reusable AI frameworks, accelerators, and shared services.
- Evaluate and recommend appropriate AI models, frameworks, and technologies based on business requirements.
- Ensure AI solutions align with enterprise architecture, cybersecurity, and governance standards.
- Define integration patterns with enterprise applications, APIs, and cloud services.

Generative AI & LLM Engineering

Lead the design and implementation of enterprise GenAI solutions including:
- Enterprise AI Copilots
- Customer Service AI Assistants
- Sales & Relationship Manager Assistants
- Loan Underwriting Assistants
- AI Knowledge Assistants
- Document Intelligence Solutions
- AI Search Platforms
- Conversational AI
- Voice AI
- Multi-Agent AI Systems
- Autonomous Workflow Agents

Develop Enterprise Solutions Using

- Retrieval-Augmented Generation (RAG)
- Prompt Engineering
- Context Management
- Agent Orchestration
- Semantic Search
- Vector Databases
- Function Calling
- AI Workflow Automation

AI Engineering Leadership

- Lead and mentor a team of GenAI Engineers, ML Engineers, and AI Developers.
- Conduct technical design reviews and code reviews.




- Establish AI engineering standards and development best practices.
- Drive innovation and continuous improvement.
- Ensure high-quality, secure, and maintainable code.
- Resolve technical challenges and guide the team on architecture decisions.

Machine Learning Engineering

Guide The Development And Deployment Of

- Predictive Models
- Classification Models
- Recommendation Engines
- Customer Segmentation Models
- Fraud Detection Models
- Credit Scoring Models
- Risk Prediction Models
- NLP Applications
- Time-Series Forecasting Models

Support the team in model optimization, validation, deployment, and monitoring.

Enterprise AI Platform Development

Work With AI Platform And MLOps Teams To

- Build scalable AI platforms.
- Develop reusable APIs and AI microservices.
- Implement model deployment pipelines.
- Manage model versioning and lifecycle.
- Establish model monitoring and observability.
- Optimize infrastructure utilization and cost.

Enterprise Integration

Lead AI Integration With Enterprise Platforms Including

- CRM
- Loan Origination Systems (LOS)
- Loan Management Systems (LMS)
- Mobile Applications
- Data Lake
- Customer Portals
- Contact Centre Platforms
- Marketing Automation Platforms
- API Gateway
- Enterprise Service Bus (ESB)
- Payment Systems
- FinTech Integrations

Cloud & DevOps

- Build cloud-native AI solutions.
- Design scalable deployment architectures.
- Optimize AI infrastructure for performance and cost.




- Implement CI/CD pipelines for AI applications.
- Collaborate with DevOps teams for deployment automation.
- Ensure high availability and disaster recovery readiness.

Security & Responsible AI

- Implement secure AI development practices.
- Ensure compliance with enterprise security policies.
- Build explainable and transparent AI solutions.
- Work closely with AI Governance teams to ensure Responsible AI practices.
- Support security audits and regulatory compliance requirements.

Stakeholder Collaboration

Work Closely With

- AI Product Manager
- AI Solution Architect
- Head - AI & Intelligent Automation
- Business Heads
- Enterprise Architecture Team
- Information Security
- Infrastructure Teams
- Data Engineering Teams
- Compliance
- Risk
- Operations
- External Technology Partners

Translate business requirements into scalable technical solutions.

Educational Qualifications

- Bachelor's Degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related field.

Preferred

- Master's Degree in Computer Science, Artificial Intelligence, Machine Learning, or Data Science.

Certifications (Preferred)

- Microsoft Azure AI Engineer Associate
- AWS Certified Machine Learning - Specialty
- Google Professional Machine Learning Engineer
- Databricks Machine Learning Qualified
- Kubernetes Certification
- TOGAF Certification
- Azure Solutions Architect Expert

Experience

- 10-14 years of overall software engineering experience.
- Minimum 5 years of experience designing AI/ML solutions.
- Minimum 3 years of experience leading AI engineering teams.
- Hands-on experience building and deploying production-grade GenAI applications.
- Experience in Banking, NBFC, Insurance, Financial Services, or FinTech preferred.

📌 Chief manager - application (Mumbai)
🏢 HDB FINANCIAL SERVICES
📍 Mumbai